{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-07-04T08:57:06.229057Z",
     "end_time": "2023-07-04T08:57:06.241026Z"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import gym\n",
    "import numpy as np\n",
    "import collections\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import rl_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# DQN\n",
    "## Q-learning\n",
    "- 不直接更新策略\n",
    "- 基于值得方法\n",
    "- Q学习算法学习一个由theta作为参数的函数Q(theta)(s,a)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "class ReplayBuffer:\n",
    "    \"\"\"\n",
    "    经验回放池\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, capacity):\n",
    "        # 队列：先进先出\n",
    "        self.buffer = collections.deque(maxlen=capacity)\n",
    "\n",
    "    def add(self, state, action, reward, next_state, done):  #将数据加入buffer\n",
    "        \"\"\"\n",
    "\n",
    "        :param state: 当前状态\n",
    "        :param action: 动作\n",
    "        :param reward: 奖励\n",
    "        :param next_state: 下一步状态\n",
    "        :param done: 是否结束\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        self.buffer.append((state, action, reward, next_state, done))\n",
    "\n",
    "    def sample(self, batch_size):  # 从buffer中采样数据，数量为batch_size\n",
    "        \"\"\"\n",
    "        从buffer中采样数据\n",
    "        :param batch_size:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        transitions = random.sample(self.buffer, batch_size)\n",
    "        state, action, reward, next_state, done = zip(*transitions)\n",
    "        return np.array(state), action, reward, np.array(next_state), done\n",
    "\n",
    "    def size(self):\n",
    "        \"\"\"\n",
    "        目前buffer中数据的数量\n",
    "        \"\"\"\n",
    "        return len(self.buffer)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T08:57:06.238036Z",
     "end_time": "2023-07-04T08:57:06.268980Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "class Qnet(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    只有一层隐藏层的Q网络\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return self.fc2(x)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T09:58:28.569687Z",
     "end_time": "2023-07-04T09:58:28.590631Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "class DQN:\n",
    "    \"\"\"\n",
    "    DQN算法\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, learning_rate, gamma, epsilon, target_update, device):\n",
    "        \"\"\"\n",
    "\n",
    "        :param state_dim:\n",
    "        :param hidden_dim:\n",
    "        :param action_dim:\n",
    "        :param learning_rate:\n",
    "        :param gamma:\n",
    "        :param epsilon:\n",
    "        :param target_update:\n",
    "        :param device:\n",
    "        \"\"\"\n",
    "        self.action_dim = action_dim\n",
    "        self.q_net = Qnet(state_dim, hidden_dim, self.action_dim).to(device)\n",
    "        #目标网络\n",
    "        self.target_q_net = Qnet(state_dim, hidden_dim, self.action_dim).to(device)\n",
    "        # 使用Adam优化器\n",
    "        self.optimizer = torch.optim.Adam(self.q_net.parameters(), lr=learning_rate)\n",
    "        self.gamma = gamma  # 折扣因子\n",
    "        self.epsilon = epsilon  # epsilon-贪婪策略\n",
    "        # 目标网络更新频率\n",
    "        self.target_update = target_update\n",
    "        self.count = 0\n",
    "        self.device = device\n",
    "\n",
    "    def take_action(self, state):\n",
    "        \"\"\"\n",
    "        epsilon-贪婪策略\n",
    "        :param state:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        if np.random.random() < self.epsilon:\n",
    "            action = np.random.randint(self.action_dim)\n",
    "        else:\n",
    "            state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "            action = self.q_net(state).argmax().item()\n",
    "        return action\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        \"\"\"\n",
    "        核心策略\n",
    "        :param transition_dict:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        states = torch.tensor(transition_dict['states'], dtype=torch.float).to(self.device)\n",
    "        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(self.device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'], dtype=torch.float).to(self.device)\n",
    "        dones = torch.tensor(transition_dict['dones'], dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        q_values = self.q_net(states).gather(1, actions)  # Q值\n",
    "        # 下个状态下的最大Q值\n",
    "        max_next_q_values = self.target_q_net(next_states).max(1)[0].view(-1, 1)\n",
    "        #TD误差目标|\n",
    "        q_targets = rewards + self.gamma * max_next_q_values * (1 - dones)\n",
    "        # 均方误差损失函数\n",
    "        dqn_loss = torch.mean(F.mse_loss(q_values, q_targets))\n",
    "        # Pytorch中默认梯度会积累，这里需要显式将梯度置于0\n",
    "        self.optimizer.zero_grad()\n",
    "        # 反向传播更新参数\n",
    "        dqn_loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        if self.count % self.target_update == 0:\n",
    "            # 更新目标网络\n",
    "            self.target_q_net.load_state_dict(self.q_net.state_dict())\n",
    "        self.count += 1"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T09:58:29.068943Z",
     "end_time": "2023-07-04T09:58:29.100857Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0:   0%|          | 0/50 [00:00<?, ?it/s]E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\utils\\passive_env_checker.py:241: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
      "  if not isinstance(terminated, (bool, np.bool8)):\n",
      "Iteration 0: 100%|██████████| 50/50 [00:00<00:00, 184.31it/s, episode=50, return=9.000]\n",
      "Iteration 1: 100%|██████████| 50/50 [00:02<00:00, 18.30it/s, episode=100, return=17.900]\n",
      "Iteration 2: 100%|██████████| 50/50 [00:21<00:00,  2.34it/s, episode=150, return=199.700]\n",
      "Iteration 3: 100%|██████████| 50/50 [00:29<00:00,  1.69it/s, episode=200, return=110.000]\n",
      "Iteration 4: 100%|██████████| 50/50 [00:35<00:00,  1.41it/s, episode=250, return=196.400]\n",
      "Iteration 5: 100%|██████████| 50/50 [00:32<00:00,  1.55it/s, episode=300, return=197.700]\n",
      "Iteration 6: 100%|██████████| 50/50 [00:36<00:00,  1.38it/s, episode=350, return=189.400]\n",
      "Iteration 7: 100%|██████████| 50/50 [00:33<00:00,  1.48it/s, episode=400, return=192.900]\n",
      "Iteration 8: 100%|██████████| 50/50 [00:29<00:00,  1.71it/s, episode=450, return=197.900]\n",
      "Iteration 9: 100%|██████████| 50/50 [00:30<00:00,  1.62it/s, episode=500, return=197.200]\n"
     ]
    }
   ],
   "source": [
    "lr = 2e-3\n",
    "num_episodes = 500\n",
    "hidden_dim = 128\n",
    "gamma = 0.98\n",
    "epsilon = 0.01\n",
    "target_update = 10\n",
    "buffer_size = 10000\n",
    "minimal_size = 500\n",
    "batch_size = 64\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "env_name = 'CartPole-v0'\n",
    "env = gym.make(env_name)\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "env.seed(0)\n",
    "torch.manual_seed(0)\n",
    "replay_buffer = ReplayBuffer(buffer_size)\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.n\n",
    "agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon, target_update, device)\n",
    "return_list = []\n",
    "for i in range(10):\n",
    "    with tqdm(total=int(num_episodes / 10), desc='Iteration %d' % i) as pbar:\n",
    "        for i_episode in range(int(num_episodes / 10)):\n",
    "            episodes_return = 0\n",
    "            state = env.reset()\n",
    "            done = False\n",
    "            while not done:\n",
    "                action = agent.take_action(state)\n",
    "                next_state, reward, done, _ = env.step(action)\n",
    "                replay_buffer.add(state, action, reward, next_state, done)\n",
    "                state = next_state\n",
    "                episodes_return += reward\n",
    "                # 当buffer数据超过一定值之后才能进行Q训练\n",
    "                if replay_buffer.size() > minimal_size:\n",
    "                    b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(batch_size)\n",
    "                    transition_dict = {\n",
    "                        'states': b_s,\n",
    "                        'actions': b_a,\n",
    "                        'next_states': b_ns,\n",
    "                        'rewards': b_r,\n",
    "                        'dones': b_d\n",
    "                    }\n",
    "                    agent.update(transition_dict)\n",
    "            return_list.append(episodes_return)\n",
    "            if (i_episode + 1) % 10 == 0:\n",
    "                pbar.set_postfix({\n",
    "                    'episode': '%d' % (num_episodes / 10 * i + i_episode + 1),\n",
    "                    'return': '%.3f' % np.mean(return_list[-10:])\n",
    "                })\n",
    "            pbar.update(1)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T09:58:29.503871Z",
     "end_time": "2023-07-04T10:02:41.114368Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "episodes_list = list(range(len(return_list)))\n",
    "plt.plot(episodes_list, return_list)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.legend()\n",
    "plt.title('DQN on {}'.format(env_name))\n",
    "plt.show()\n",
    "\n",
    "mv_return = rl_utils.moving_average(return_list, 9)\n",
    "plt.plot(episodes_list, mv_return)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('DQN on {}'.format(env_name))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T10:02:41.114368Z",
     "end_time": "2023-07-04T10:02:41.444885Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
